Kriging and Local Polynomial Methods for Blending Satellite-Derived and Gauge Precipitation Estimates to Support Hydrologic Early Warning Systems

• Published in: IEEE transactions on geoscience and remote sensing Vol. 54; no. 5; pp. 2552 - 2562
• Main Authors: Verdin, Andrew, Funk, Chris, Rajagopalan, Balaji, Kleiber, William
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Blending; Blending data; Estimates; Gages; Gauges; Hazards; hydrologic early warning systems; Kriging; local polynomials; Microwave imaging; Microwave measurement; Microwave theory and techniques; ordinary kriging; Polynomials; Precipitation; Rain; Rainfall; rainfall estimation; Satellites; ordinary kriging; local polynomials; Blending data; rainfall estimation; hydrologic early warning systems
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2015.2502956

Robust estimates of precipitation in space and time are important for efficient natural resource management and for mitigating natural hazards. This is particularly true in regions with developing infrastructure and regions that are frequently exposed to extreme events. Gauge observations of rainfall are sparse but capture the precipitation process with high fidelity. Due to its high resolution and complete spatial coverage, satellite-derived rainfall data are an attractive alternative in data-sparse regions and are often used to support hydrometeorological early warning systems. Satellite-derived precipitation data, however, tend to underrepresent extreme precipitation events. Thus, it is often desirable to blend spatially extensive satellite-derived rainfall estimates with high-fidelity rain gauge observations to obtain more accurate precipitation estimates. In this research, we use two different methods, namely, ordinary kriging and κ-nearest neighbor local polynomials, to blend rain gauge observations with the Climate Hazards Group Infrared Precipitation satellite-derived precipitation estimates in data-sparse Central America and Colombia. The utility of these methods in producing blended precipitation estimates at pentadal (five-day) and monthly time scales is demonstrated. We find that these blending methods significantly improve the satellite-derived estimates and are competitive in their ability to capture extreme precipitation.